CVPR 2020 Workshop on

Adversarial Machine Learning in Computer Vision

Seattle, Washington
June, 2020


Although computer vision models have achieved advanced performance on various recognition tasks in recent years, they are known to be vulnerable against adversarial examples. The existence of adversarial examples reveals that current computer vision models perform differently with the human vision system, and on the other hand provides opportunities for understanding and improving these models.

In this workshop, we will focus on recent research and future directions on adversarial machine learning in computer vision. We aim to bring experts from the computer vision, machine learning and security communities together to highlight the recent progress in this area, as well as discuss the benefits of integrating recent progress in adversarial machine learning into general computer vision tasks. Specifically, we seek to study adversarial machine learning not only for enhancing the model robustness against adversarial attacks, but also as a guide to diagnose/explain the limitation of current computer vision models as well as potential improving strategies. We hope this workshop can shed light on bridging the gap between the human vision system and computer vision systems, and chart out cross-community collaborations, including computer vision, machine learning and security communities.

Call For Papers

Submission deadline (Tentative): March 15, 2020 Anywhere on Earth (AoE)

Notification sent to authors (Tentative): April 3, 2020 Anywhere on Earth (AoE)

Camera ready deadline (Tentative): April 10, 2020 Anywhere on Earth (AoE)

Submission server:

The workshop will include contributed papers. Based on the PC’s recommendation, each paper accepted to the workshop will be allocated either a contributed talk or a poster presentation.

Submissions need to be anonymized, and follow the CVPR 2020 Submission Guidelines. The workshop will have official proceedings.

We invite submissions on any aspect of adversarial machine learning in computer vision. This includes, but is not limited to:




Organizing Committee

Program Committee (TBU)

  • Maksym Andriushchenko (EPFL)
  • Anurag Arnab (Google)
  • Arjun Nitin Bhagoji (Princeton University)
  • Wieland Brendel (University of Tübingen)
  • Yulong Cao (University of Michigan)
  • Hongge Chen (MIT)
  • Ambra Demontis (University of Cagliari)
  • Yinpeng Dong (Tsinghua University)
  • Sven Gowal (DeepMind)(/li)
  • Chuan Guo (Cornell University)
  • Saumya Jetley (University of Oxford)
  • Alexey Kurakin (Google Brain)
  • Yingwei Li (Johns Hopkins University)
  • Jan Hendrik Metzen (Bosch Center for Artificial Intelligence)
  • Mahyar Najibi (University of Maryland, College Park)
  • Tianyu Pang (Tsinghua University)
  • Maura Pintor (University of Cagliari)
  • Hamed Pirsiavash (University of Maryland, Baltimore County)
  • Omid Poursaeed (Cornell University)
  • Aaditya Prakash (PathAI)
  • Chongli Qin (DeepMind)
  • Jonas Rauber (University of Tübingen)
  • Ali Shafahi (University of Maryland, College Park)
  • Yash Sharma (University of Tübingen)
  • Krishna Kumar Singh (UC Davis)
  • David Stutz (Max Planck Institute for Informatics)
  • Jianyu Wang (Baidu USA)
  • Yuxin Wu (Facebook AI Research)
  • Chang Xiao (Columbia University)
  • Chaowei Xiao (University of Michigan)
  • Hongyang Zhang (Toyota Technological Institute at Chicago)
  • Huan Zhang (UCLA)

Please contact Cihang Xie or Xinyun Chen if you have questions. The webpage template is by the courtesy of ICCV 2019 Tutorial on Interpretable Machine Learning for Computer Vision. Thank Yingwei Li for making this website.